class_docstr: `ImageNetv2 Dataset <https://arxiv.org/abs/1902.10811>`_ for studying the robustness of models trained on ImageNet dataset
  
  Args:
      opts: command-line arguments
class_docstr_tok: ["`","ImageNetv2","Dataset","<","https",":","\/\/arxiv.org\/abs\/1902.10811",">","`","_","for","studying","the","robustness","of","models","trained","on","ImageNet","dataset","Args",":","opts",":","command-line","arguments"]
docstr: Data transforms during validation
  
  Order of transform is Resize, CenterCrop, ToTensor
  
  Returns:
      An instance of `data.transforms.image_pil.BaseTransformation.`
docstr_tok: ["Data","transforms","during","validation","Order","of","transform","is","Resize",",","CenterCrop",",","ToTensor","Returns",":","An","instance","of","`","data.transforms.image_pil.BaseTransformation.","`"]
code: def _validation_transforms(self, *args, **kwargs):
      """Data transforms during validation
  
      Order of transform is Resize, CenterCrop, ToTensor
  
      Returns:
          An instance of `data.transforms.image_pil.BaseTransformation.`
      """
      aug_list = [
          T.Resize(opts=self.opts),
          T.CenterCrop(opts=self.opts),
          T.ToTensor(opts=self.opts),
      ]
  
      return Compose(opts=self.opts, img_transforms=aug_list)
  
file_path: ml-cvnets/data/datasets/classification/imagenet_v2.py
imports: import argparse
  import tarfile
  from pathlib import Path
  from typing import Dict, Tuple
  import torch
  from data.datasets import DATASET_REGISTRY
  from data.datasets.dataset_base import BaseImageDataset
  from data.transforms import image_pil
  from data.transforms.common import Compose
  from utils import logger
  from utils.download_utils import get_local_path
label_desc: Use image node_id 3 for calling the Imagenetv2Dataset obj's underlying member method code with example usage: obj._validation_transforms() and returns: Compose